class_docstr: A preprocessing layer which resizes images.
  
  This layer resizes an image input to a target height and width. The input
  should be a 4D (batched) or 3D (unbatched) tensor in `"channels_last"`
  format. Input pixel values can be of any range
  (e.g. `[0., 1.)` or `[0, 255]`).
  
  Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format,
      or `(..., channels, height, width)`, in `"channels_first"` format.
  
  Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., target_height, target_width, channels)`,
      or `(..., channels, target_height, target_width)`,
      in `"channels_first"` format.
  
  **Note:** This layer is safe to use inside a `tf.data` pipeline
  (independently of which backend you're using).
  
  Args:
      height: Integer, the height of the output shape.
      width: Integer, the width of the output shape.
      interpolation: String, the interpolation method.
          Supports `"bilinear"`, `"nearest"`, `"bicubic"`,
          `"lanczos3"`, `"lanczos5"`. Defaults to `"bilinear"`.
      crop_to_aspect_ratio: If `True`, resize the images without aspect
          ratio distortion. When the original aspect ratio differs
          from the target aspect ratio, the output image will be
          cropped so as to return the
          largest possible window in the image (of size `(height, width)`)
          that matches the target aspect ratio. By default
          (`crop_to_aspect_ratio=False`), aspect ratio may not be preserved.
      data_format: string, either `"channels_last"` or `"channels_first"`.
          The ordering of the dimensions in the inputs. `"channels_last"`
          corresponds to inputs with shape `(batch, height, width, channels)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch, channels, height, width)`. It defaults to the
          `image_data_format` value found in your Keras config file at
          `~/.keras/keras.json`. If you never set it, then it will be
          `"channels_last"`.
      **kwargs: Base layer keyword arguments, such as `name` and `dtype`.
code: def __init__(
      self,
      height,
      width,
      interpolation="bilinear",
      crop_to_aspect_ratio=False,
      data_format=None,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.height = height
      self.width = width
      self.interpolation = interpolation
      self.data_format = backend.standardize_data_format(data_format)
      self.crop_to_aspect_ratio = crop_to_aspect_ratio
  
file_path: keras/keras/layers/preprocessing/resizing.py
imports: from keras import backend
  from keras.api_export import keras_export
  from keras.layers.preprocessing.tf_data_layer import TFDataLayer
  from keras.utils import image_utils
label_desc: Use image node_id 1 to create a new Resizing object from inherited base classes: TFDataLayer with example: obj = Resizing(height, width, interpolation, crop_to_aspect_ratio, data_format)